code: def verify_pretrain_params(args):
      """
      Function which verifies the model params in case we are using a pretrained
      model like ELMo. This is needed because the model params must match up
      with the weights that are being loaded.
      """
      assert (
          args.embed_bytes
      ), "To use pretrained weights, embed_bytes must be set to True."
  
      assert (
          args.char_cnn_nonlinear_fn
          == model_constants.PRETRAINED_CHAR_CNN_NONLINEAR_FN
      ), "To use pretrained weights, the non linearity used should be relu."
  
      assert (
          args.char_embed_dim
          == model_constants.PRETRAINED_CHAR_EMBED_DIM
      ), "To use pretrained weights char_embed_dim should be set to 16."
  
      assert (
          args.char_cnn_output_dim
          == model_constants.PRETRAINED_CHAR_CNN_OUTPUT_DIM
      ), "To use pretrained weights, the output dim of the CNN layer should be 512."
      assert (
          literal_eval(args.char_cnn_params)
          == model_constants.PRETRAINED_CHAR_CNN_PARAMS
      ), "CNN Params don't match with the ones needed for loading pretrained weights"
  
imports: import logging
  from ast import literal_eval
  import torch
  import torch.nn.functional
  from fairseq.models import FairseqEncoder, register_model, register_model_architecture
  from pytorch_translate import char_encoder, model_constants, rnn, utils, vocab_constants
  from pytorch_translate.common_layers import Embedding, VariableTracker
  from pytorch_translate.data.dictionary import TAGS
label_desc: Use image node_id 1 for calling a global function with example usage: verify_pretrain_params(args) without return types